Efficient Capital Markets Explained - YouTube

- The information in this video might be more important

than anything that I've ever told you.

In every one of my videos,

I tell you things that hinge on one of the landmark ideas

in financial economics,

the efficiency of the capital markets.

As fundamental as market efficiency is

to good financial decision-making,

it is poorly understood by most investors.

I have been telling you information

that is based on this foundational principle of finance,

but I've never taken the time to explain to you

why it is so important.

Condensing the foundations of modern financial theory

into a short video is ambitious,

but I'm going to do my best.

I'm Ben Felix, portfolio manager at PWL Capital.

In this episode of Common Sense Investing,

I'm going to tell you why the efficient market hypothesis

matters to your investment decisions.

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Financial theory starts with asset pricing.

How do investors decide how much to pay for a stock?

Theoretically, rational investors are willing

to pay a price for a stock based on the present value

of expected future earnings.

All else equal, investors will pay more

for higher expected earnings

and they will pay less for riskier future earnings.

Whether or not investors

are rationally evaluating investments this way,

brings us to the concept of market efficiency.

As defined by Eugene Fama in 1970,

a market in which prices always "fully reflect"

available information is called "efficient".

This is a simple sounding concept,

but it is also extremely important

and terribly misunderstood.

Informational efficiency in a market

stems from competition for profits, low transaction costs,

and readily available information.

If there is information suggesting that an asset's value

will be higher in the future,

competitive traders with low cost access to the market

will buy that asset today, increasing its price.

The competition to incorporate new information into prices

for profit, means that prices should change quickly

as new information develops.

New information is random,

and therefore price changes should be random.

This point is at the core of index investing.

If price changes are based on new information

and therefore not predictable,

then trying to predict them

as any traditional active manager or stock picker does,

should not be expected to improve your outcome.

And that's before the higher costs and risks

of active management are considered.

In an efficient market,

low-cost index investing makes sense.

Explaining why market efficiency matters is easy to do.

I just did it in one sentence.

Explaining how we know that efficient markets

reflect reality is a much bigger task,

but it is a task worth undertaking.

To continue this discussion,

I need to make sure that we all have

the same baseline understanding of a few concepts,

starting with the meaning of the words,

"empirical" and "theoretical".

Empirical research means looking at real data.

For example, noticing that day-to-day changes

in stock prices are random, is an empirical observation.

Empirical observations are concrete,

but drawing insight from empirical observations

is more abstract.

This is where we see the importance of theory.

Theory is an idea about why things work

the way that they do.

In financial economics, empirical research and theory

are tied together by scientific method,

forming a hypothesis, collecting data,

and testing the hypothesis.

If economists make consistent empirical observations

about stock returns, they might develop a hypothesis

about what is driving that empirical result.

Once a hypothesis has been created,

it can be tested in the data.

If a theory ends up doing a good job of explaining reality,

it becomes a useful decision tool.

In 1900, 70 years before Fama published

his landmark work on market efficiency,

Louie Bachelier, a French statistician,

noticed that stock prices seem to follow a random walk.

In the 1950s and 1960s, more empirical work emerged,

suggesting that stock prices moved randomly.

With no theoretical explanation for this randomness,

economists at the time concluded that stock prices

did not have any economic meaning.

In 1965, Paul Samuelson brought forward the idea

that in a well-functioning and competitive market,

we would expect prices to change

as investors' expectations adapt to new information.

This theoretical explanation called the "fair game model",

started to bring meaning to the randomness of stock prices.

The work of Samuelson was followed

by Fama's now famous 1970 paper,

"Efficient Capital Markets:

"A Review of Theory and Empirical Work."

A simple summary of past work

is not what made Fama's paper famous.

Fama formalized an empirical approach

to testing the theory of market efficiency.

Fama never claimed that the market is perfectly efficient.

Perfect efficiency is an ideal state

that real markets can only approach.

That is not a shortcoming of the efficient hypothesis.

Theory is not meant to be reality.

The map is not the territory.

Theory allows us to predict what the world might look like

in an ideal state and compare that prediction to reality.

In the case of efficient markets,

reality does look very similar

to what we would predict an efficient market to look like.

Market efficiency predicts that prices should move randomly.

Active managers should not be successful

at beating the market,

and prices should change quickly based on new information.

Each of these predictions describe real markets.

Stock price changes are random.

Active managers do on average, trail the market after costs.

Even managers with the best returns are no more likely

to have strong future returns.

Event studies that is observing X post

how prices moved based on new information

have indicated that markets are generally very quick

to incorporate new information.

One of the challenges with the efficient market hypothesis

is that it cannot be definitively proved or disproved.

This was acknowledged by Fama

as the Joint Hypothesis Theorem.

Any attempt to test market efficiency

is really a test of two distinct hypothesis.

It is jointly a test of the efficient market hypothesis,

and a test of the model of market equilibrium.

What I just said will make sense in a second, stick with me.

The model of market equilibrium

is the model of how the market prices assets.

We can think about value stocks as an example.

Under the capital asset pricing model

that is defining risk as only the risk of the market,

value stocks produce higher average returns

than would be expected based on their riskiness.

Under the capital asset pricing model

for equilibrium pricing,

value stocks violate the efficient market hypothesis.

They create a systematic excess return

that cannot be explained by risk.

This result could mean one of two things.

Markets are not efficient,

or the model being used for market equilibrium is flawed.

The implication of the joint hypothesis theorem

is that the concept of market efficiency

is only as empirically useful as the model that we have

for market equilibrium.

Specifying this equilibrium model has been the grounds

for much of the work in empirical finance since 1970.

In the case of value stocks, later research revealed

that the single factor capital asset pricing model

for market equilibrium was not accounting

for the independent risk of value stocks.

Adding the independent risk and value stocks to the model

takes away the efficient market violation.

Value stocks do not violate market efficiency,

they're just riskier than we originally understood.

Improving the equilibrium model for asset pricing

by identifying independent risks

led to the Fama French Three Factor Model in 1992,

which includes the independent risks of the market,

small stocks, and value stocks.

And the Five-Factor Model in 2014,

adding the independent risks of stocks

with robust profitability,

and stocks that invest aggressively

to the Three Factor Model.

Without the efficient market hypothesis,

empirical finance would just be a collection of anecdotes.

Efficient markets as a framework,

has allowed financial economists to evaluate theories

by their rejectable predictions,

as opposed to observing the individual outcomes

of successful investors.

We now have the ability to gain insight

into how markets work,

as opposed to relying on anecdotes

that we hope to replicate.

An anecdote is like a story,

one sample of a successful outcome

with no theoretical explanation

and no empirical corroboration.

In a social science like economics,

we want to build an understanding of the world

that allows us to make better decisions,

while avoiding the type of bias

that anecdotes often promote.

Warren Buffet or anyone else

being successful at picking stocks is an anecdote,

but asking why he was successful

is not a productive question.

It might be similar to asking a doctor

why your grandpa lived to be 98,

even though he smoked a pack of cigarettes a day.

Science does not have the explanation for every outcome

but this does not make it a good idea

to start smoking or stock-picking.

Interestingly, Buffet's performance has now been explained

within the framework of an efficient market.

He simply knew which types of risks to maintain exposure to,

and used extreme discipline and leverage to do so.

Again, we see the joint hypothesis problem.

Buffet is not proof of market inefficiency

when the appropriate model for equilibrium pricing

is specified.

As the model for market equilibrium pricing

has developed over time,

it has gotten increasingly difficult

to find violations of the efficient market hypothesis.

With the Fama French Five Factor Model,

the vast majority of differences in returns

between two diversified portfolios can be explained

by differences in exposure

to the independent risks identified in the model.

Adding more factors to a model to make it explain returns

sounds like over-fitting or data mining.

But the way that this model was developed

is another critical aspect of modern financial theory.

In their 2006 paper,

"Profitability, Investment, and Average Returns",

Fama and French documented the body of empirical work,

showing that three specific common characteristics of stocks

predict higher average returns.

Price relative to book value

where cheaper stocks have higher average returns,

profitability where all else equal,

a more profitable stock must have a higher expected return,

and investment, where all else equal,

a stock that reinvests profits conservatively

must have a higher expected return.

They took these individual empirical observations

and use the framework evaluation theory

to construct a new set of empirical tests.

Valuation theory predicts that there should be

a relationship between these three characteristics.

Past empirical work had observed them individually.

Based on the theory, properly observing

the effects of each characteristic

must be done by controlling for the other two.

Fama and French verified this empirically in 2006,

which eventually led to the Five Factor Model,

the most complete model of market equilibrium to date.

This takes us to 2014, which is when the Fama French paper

introducing the Five Factor Model came out.

We're not talking about ancient history here.

Remember, equilibrium pricing is the theoretical model

for how the market prices assets.

The Five Factor Model suggests that the market

prices more risk into certain types of assets

as identified in the Five Factor Model.

The Five Factor Model can be used

to empirically test the concept of market efficiency,

subject, of course, to the joint hypothesis problem

that we talked about earlier.

In its current form, the Five Factor Model

is able to explain over 90% of the difference in returns

between any two diversified portfolios.

The room for arguments about market inefficiency

has gotten very small.

Any returned difference that appears to be an anomaly,

like the returns of dividend paying stocks, for example,

can most likely be explained by exposure

to the risk factors identified in the model.

Understanding that this was not an exercise

in blind data mining is important.

It comes from making empirical observations,

developing logical hypothesis, and testing the hypothesis.

Today, the theory of efficient markets

and equilibrium pricing is robust enough

that in empirical testing, it explains almost any difference

in returns over any historical time period

in any country that we have data for.

It is also able to explain anomalies like low beta stocks

and even the performance of Warren Buffet.

What used to seem like proof

that the market was not efficient,

has today been explained by how assets are priced

in an efficient market.

Asset pricing is extremely important

to how investors allocate their capital.

The price that you pay for a stock

defines your expected return.

If prices are constantly wrong,

investors will not know where to allocate capital.

Fortunately, there is a strong theoretical

and empirical case that markets are efficient.

They price stocks based on their expected future earnings,

and the riskiness of those earnings.

Market efficiency has sweeping implications

on how you should invest your money.

At a bare minimum, market efficiencies should push investors

toward low cost index funds.

There is no way to exploit random stock price changes

to generate higher average returns,

so taking what the market has to offer is a smart bet.

At a more advanced level,

there is a strong, theoretical, and empirical case

that the Five Factor Asset Pricing Model

is a good model of market equilibrium.

Based on this, allocating more capital

to the types of stocks that the model predicts

to have higher expected returns could be sensible.

Finally, I think it is worth mentioning the role of evidence

in decision-making.

The medical field has a hierarchy of evidence,

ranking types of evidence based on their quality

and their risk of bias.

The lowest form of evidence

in this hierarchy is expert opinion,

while the highest is systematic reviews

of randomized controlled trials.

In the world of financial economics,

we do not have randomized controlled trials,

but we do have a nearly unlimited pool of data

for empirical testing.

There is a scientific approach

to accessing the capital markets efficiently.

Despite this, investors will be quick to listen to the words

of a successful hedge fund manager,

or the CEO of a big bank,

while being quick to dismiss the theory and evidence

that we have discussed today.

As I mentioned earlier, the map is not the territory.

The models are not perfect reflections of reality,

but we have a choice between making decisions

based on the theory and empirical data,

or making decisions based on what we hope will do well

even if that hope is at odds with the data.

Thanks for watching.

My name is Ben Felix of PWL Capital,

and this is Common Sense Investing.

If you enjoyed this video,

please share it with someone

who you think could benefit from the information.

Don't forget, if you've run out

of Common Sense Investing videos to watch,

you can tune into weekly episodes

of the Rational Reminder Podcast,

wherever you get your podcasts.

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